Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations154
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.1 KiB
Average record size in memory160.0 B

Variable types

Numeric18
Boolean8

Alerts

campaña_Verano is highly overall correlated with tem_agua and 1 other fieldsHigh correlation
campaña_invierno is highly overall correlated with clorofila_a_ug_l and 2 other fieldsHigh correlation
campaña_otoño is highly overall correlated with dqo_mg_l and 1 other fieldsHigh correlation
clorofila_a_ug_l is highly overall correlated with campaña_inviernoHigh correlation
colif_fecales_ufc_100ml is highly overall correlated with icaHigh correlation
dqo_mg_l is highly overall correlated with campaña_otoñoHigh correlation
enteroc_ufc_100ml is highly overall correlated with espumasHigh correlation
espumas is highly overall correlated with enteroc_ufc_100mlHigh correlation
fosf_ortofos_mg_l is highly overall correlated with ica and 1 other fieldsHigh correlation
ica is highly overall correlated with colif_fecales_ufc_100ml and 3 other fieldsHigh correlation
microcistina_ug_l is highly overall correlated with tem_agua and 1 other fieldsHigh correlation
nitrato_mg_l is highly overall correlated with campaña_inviernoHigh correlation
od is highly overall correlated with phHigh correlation
olores is highly overall correlated with icaHigh correlation
p_total_l_mg_l is highly overall correlated with fosf_ortofos_mg_l and 1 other fieldsHigh correlation
ph is highly overall correlated with odHigh correlation
tem_agua is highly overall correlated with campaña_Verano and 3 other fieldsHigh correlation
tem_aire is highly overall correlated with campaña_Verano and 2 other fieldsHigh correlation
turbiedad_ntu is highly overall correlated with campaña_otoñoHigh correlation
olores is highly imbalanced (60.5%) Imbalance
color is highly imbalanced (58.2%) Imbalance
espumas is highly imbalanced (79.3%) Imbalance
clorofila_a_ug_l has 5 (3.2%) zeros Zeros

Reproduction

Analysis started2024-11-09 15:32:07.770777
Analysis finished2024-11-09 15:33:28.662738
Duration1 minute and 20.89 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

tem_agua
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.900195
Minimum6
Maximum27.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:28.916832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10
Q115.0325
median18.03
Q320.4
95-th percentile25.505
Maximum27.4
Range21.4
Interquartile range (IQR)5.3675

Descriptive statistics

Standard deviation4.734447
Coefficient of variation (CV)0.26449137
Kurtosis-0.33114731
Mean17.900195
Median Absolute Deviation (MAD)2.95
Skewness-0.12529167
Sum2756.63
Variance22.414989
MonotonicityNot monotonic
2024-11-09T12:33:29.274717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 7
 
4.5%
20 6
 
3.9%
18.5 5
 
3.2%
18.6 5
 
3.2%
17 4
 
2.6%
23 3
 
1.9%
18.2 3
 
1.9%
24.7 3
 
1.9%
15.6 3
 
1.9%
17.1 3
 
1.9%
Other values (90) 112
72.7%
ValueCountFrequency (%)
6 1
 
0.6%
7 2
 
1.3%
8 2
 
1.3%
9 1
 
0.6%
10 7
4.5%
10.01 1
 
0.6%
11 1
 
0.6%
11.01 1
 
0.6%
12 1
 
0.6%
12.7 2
 
1.3%
ValueCountFrequency (%)
27.4 1
0.6%
27 1
0.6%
26.3 1
0.6%
26.1 2
1.3%
26 1
0.6%
25.8 1
0.6%
25.7 1
0.6%
25.4 2
1.3%
25.2 1
0.6%
25.1 1
0.6%

tem_aire
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.758571
Minimum4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:29.548328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q113
median15
Q318
95-th percentile25.2
Maximum27
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.8926195
Coefficient of variation (CV)0.31047355
Kurtosis-0.082644355
Mean15.758571
Median Absolute Deviation (MAD)2
Skewness0.45626231
Sum2426.82
Variance23.937726
MonotonicityNot monotonic
2024-11-09T12:33:29.895774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
14 25
16.2%
13 15
 
9.7%
16 13
 
8.4%
12 12
 
7.8%
17 9
 
5.8%
15 7
 
4.5%
22 6
 
3.9%
23.3 5
 
3.2%
8 5
 
3.2%
10 5
 
3.2%
Other values (26) 52
33.8%
ValueCountFrequency (%)
4 1
 
0.6%
5 1
 
0.6%
6 1
 
0.6%
7 1
 
0.6%
8 5
3.2%
9 3
 
1.9%
10 5
3.2%
11 4
 
2.6%
12 12
7.8%
12.3 1
 
0.6%
ValueCountFrequency (%)
27 5
3.2%
26 1
 
0.6%
25.2 3
1.9%
25 1
 
0.6%
23.3 5
3.2%
23 5
3.2%
22.2 2
 
1.3%
22 6
3.9%
21 2
 
1.3%
20 4
2.6%

od
Real number (ℝ)

High correlation 

Distinct149
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7581169
Minimum0.36
Maximum17.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:30.191542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.36
5-th percentile2.2025
Q15.315
median6.757
Q38.348
95-th percentile10.8475
Maximum17.61
Range17.25
Interquartile range (IQR)3.033

Descriptive statistics

Standard deviation2.6593861
Coefficient of variation (CV)0.39350993
Kurtosis1.2625353
Mean6.7581169
Median Absolute Deviation (MAD)1.53
Skewness0.21749593
Sum1040.75
Variance7.0723343
MonotonicityNot monotonic
2024-11-09T12:33:30.567425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.36 2
 
1.3%
7 2
 
1.3%
9 2
 
1.3%
4.28 2
 
1.3%
7.85 2
 
1.3%
3.5 1
 
0.6%
1.5 1
 
0.6%
6.3 1
 
0.6%
4.49 1
 
0.6%
3.85 1
 
0.6%
Other values (139) 139
90.3%
ValueCountFrequency (%)
0.36 1
0.6%
0.45 1
0.6%
1.02 1
0.6%
1.13 1
0.6%
1.39 1
0.6%
1.5 1
0.6%
1.8 1
0.6%
2.17 1
0.6%
2.22 1
0.6%
2.25 1
0.6%
ValueCountFrequency (%)
17.61 1
0.6%
12.84 1
0.6%
12.15 1
0.6%
12 1
0.6%
11.82 1
0.6%
11.05 1
0.6%
11.02 1
0.6%
10.88 1
0.6%
10.83 1
0.6%
10.6 1
0.6%

ph
Real number (ℝ)

High correlation 

Distinct114
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5916234
Minimum5
Maximum10.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:30.916422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.56
Q17.145
median7.54
Q37.99
95-th percentile8.817
Maximum10.02
Range5.02
Interquartile range (IQR)0.845

Descriptive statistics

Standard deviation0.68613411
Coefficient of variation (CV)0.09038042
Kurtosis2.3340791
Mean7.5916234
Median Absolute Deviation (MAD)0.408
Skewness0.41903492
Sum1169.11
Variance0.47078001
MonotonicityNot monotonic
2024-11-09T12:33:31.316839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4 7
 
4.5%
7.58 4
 
2.6%
7.6 4
 
2.6%
7.5 3
 
1.9%
7.3 3
 
1.9%
7.76 3
 
1.9%
7.67 3
 
1.9%
8.02 3
 
1.9%
7.8 3
 
1.9%
7.99 3
 
1.9%
Other values (104) 118
76.6%
ValueCountFrequency (%)
5 1
0.6%
6.2 1
0.6%
6.37 1
0.6%
6.39 1
0.6%
6.48 1
0.6%
6.53 1
0.6%
6.54 1
0.6%
6.56 2
1.3%
6.59 1
0.6%
6.66 1
0.6%
ValueCountFrequency (%)
10.02 1
0.6%
9.98 1
0.6%
9.39 1
0.6%
9.17 1
0.6%
9.16 1
0.6%
9.01 1
0.6%
8.95 1
0.6%
8.83 1
0.6%
8.81 1
0.6%
8.62 1
0.6%

olores
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
142 
True
 
12
ValueCountFrequency (%)
False 142
92.2%
True 12
 
7.8%
2024-11-09T12:33:31.648620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

color
Boolean

Imbalance 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
141 
True
 
13
ValueCountFrequency (%)
False 141
91.6%
True 13
 
8.4%
2024-11-09T12:33:31.897901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

espumas
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
149 
True
 
5
ValueCountFrequency (%)
False 149
96.8%
True 5
 
3.2%
2024-11-09T12:33:32.122353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

mat_susp
Boolean

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
127 
True
27 
ValueCountFrequency (%)
False 127
82.5%
True 27
 
17.5%
2024-11-09T12:33:32.324366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

colif_fecales_ufc_100ml
Real number (ℝ)

High correlation 

Distinct89
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86134.578
Minimum80
Maximum4200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:32.610527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile268.5
Q11200
median3900
Q337500
95-th percentile273500
Maximum4200000
Range4199920
Interquartile range (IQR)36300

Descriptive statistics

Standard deviation380085.74
Coefficient of variation (CV)4.4126964
Kurtosis92.357448
Mean86134.578
Median Absolute Deviation (MAD)3320
Skewness8.9943955
Sum13264725
Variance1.4446517 × 1011
MonotonicityNot monotonic
2024-11-09T12:33:32.961134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 6
 
3.9%
1000 5
 
3.2%
1400 5
 
3.2%
1800 5
 
3.2%
900 4
 
2.6%
3000 4
 
2.6%
1300 4
 
2.6%
40000 4
 
2.6%
400 3
 
1.9%
6500 3
 
1.9%
Other values (79) 111
72.1%
ValueCountFrequency (%)
80 1
0.6%
95 1
0.6%
120 1
0.6%
130 1
0.6%
150 1
0.6%
160 1
0.6%
200 1
0.6%
210 1
0.6%
300 2
1.3%
360 1
0.6%
ValueCountFrequency (%)
4200000 1
0.6%
1600000 1
0.6%
1070000 1
0.6%
740000 1
0.6%
700000 1
0.6%
420000 1
0.6%
400000 1
0.6%
280000 1
0.6%
270000 1
0.6%
240000 1
0.6%

escher_coli_ufc_100ml
Real number (ℝ)

Distinct78
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4070.5779
Minimum1
Maximum150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:33.267942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.65
Q1100
median350
Q31675
95-th percentile12840
Maximum150000
Range149999
Interquartile range (IQR)1575

Descriptive statistics

Standard deviation15011.466
Coefficient of variation (CV)3.6877973
Kurtosis63.204412
Mean4070.5779
Median Absolute Deviation (MAD)331
Skewness7.3356342
Sum626869
Variance2.2534412 × 108
MonotonicityNot monotonic
2024-11-09T12:33:33.599952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 17
 
11.0%
200 15
 
9.7%
300 6
 
3.9%
600 5
 
3.2%
500 4
 
2.6%
10000 4
 
2.6%
1000 4
 
2.6%
6 4
 
2.6%
2000 3
 
1.9%
3 3
 
1.9%
Other values (68) 89
57.8%
ValueCountFrequency (%)
1 1
 
0.6%
2 2
1.3%
3 3
1.9%
4 1
 
0.6%
5 1
 
0.6%
6 4
2.6%
9 1
 
0.6%
13 1
 
0.6%
15 1
 
0.6%
16 1
 
0.6%
ValueCountFrequency (%)
150000 1
0.6%
80000 1
0.6%
50000 1
0.6%
44000 1
0.6%
35000 1
0.6%
28000 1
0.6%
15000 1
0.6%
14400 1
0.6%
12000 1
0.6%
11500 1
0.6%

enteroc_ufc_100ml
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean945.75974
Minimum2
Maximum28000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:33.924873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q150
median300
Q3667.5
95-th percentile2780
Maximum28000
Range27998
Interquartile range (IQR)617.5

Descriptive statistics

Standard deviation2999.543
Coefficient of variation (CV)3.1715698
Kurtosis54.448552
Mean945.75974
Median Absolute Deviation (MAD)270
Skewness6.9803757
Sum145647
Variance8997258.5
MonotonicityNot monotonic
2024-11-09T12:33:34.233623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7
 
4.5%
10 6
 
3.9%
300 6
 
3.9%
50 6
 
3.9%
20 5
 
3.2%
1500 5
 
3.2%
2 5
 
3.2%
30 4
 
2.6%
1100 3
 
1.9%
80 3
 
1.9%
Other values (75) 104
67.5%
ValueCountFrequency (%)
2 5
3.2%
3 1
 
0.6%
4 1
 
0.6%
5 2
 
1.3%
9 1
 
0.6%
10 6
3.9%
11 1
 
0.6%
20 5
3.2%
24 1
 
0.6%
27 1
 
0.6%
ValueCountFrequency (%)
28000 1
0.6%
20000 1
0.6%
12000 1
0.6%
7500 1
0.6%
5000 1
0.6%
4200 1
0.6%
4000 1
0.6%
3300 1
0.6%
2500 1
0.6%
2200 1
0.6%

nitrato_mg_l
Real number (ℝ)

High correlation 

Distinct84
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7038961
Minimum1.9
Maximum21.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:34.548339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile2
Q13.6
median5.65
Q38.675
95-th percentile13.57
Maximum21.9
Range20
Interquartile range (IQR)5.075

Descriptive statistics

Standard deviation4.0578639
Coefficient of variation (CV)0.60529934
Kurtosis1.0475266
Mean6.7038961
Median Absolute Deviation (MAD)2.5
Skewness1.1216834
Sum1032.4
Variance16.466259
MonotonicityNot monotonic
2024-11-09T12:33:34.917812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6
 
3.9%
3.3 5
 
3.2%
1.9 4
 
2.6%
5.1 4
 
2.6%
3.9 4
 
2.6%
3.7 4
 
2.6%
5.9 4
 
2.6%
2.9 3
 
1.9%
5.2 3
 
1.9%
5.6 3
 
1.9%
Other values (74) 114
74.0%
ValueCountFrequency (%)
1.9 4
2.6%
2 6
3.9%
2.1 2
 
1.3%
2.2 1
 
0.6%
2.4 1
 
0.6%
2.5 1
 
0.6%
2.6 3
1.9%
2.7 3
1.9%
2.8 2
 
1.3%
2.9 3
1.9%
ValueCountFrequency (%)
21.9 1
0.6%
20.6 1
0.6%
16.4 1
0.6%
16.3 1
0.6%
16.2 1
0.6%
14.8 1
0.6%
14.4 1
0.6%
13.7 1
0.6%
13.5 1
0.6%
13.3 2
1.3%

nh4_mg_l
Real number (ℝ)

Distinct84
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9411558
Minimum0.049
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:35.266333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.049
5-th percentile0.049
Q10.1025
median0.65
Q31.675
95-th percentile7.655
Maximum23
Range22.951
Interquartile range (IQR)1.5725

Descriptive statistics

Standard deviation4.1198027
Coefficient of variation (CV)2.1223452
Kurtosis16.287446
Mean1.9411558
Median Absolute Deviation (MAD)0.59
Skewness3.9375779
Sum298.938
Variance16.972774
MonotonicityNot monotonic
2024-11-09T12:33:35.573929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05 18
 
11.7%
0.049 12
 
7.8%
2 5
 
3.2%
0.1 5
 
3.2%
0.41 5
 
3.2%
1 4
 
2.6%
1.9 3
 
1.9%
0.45 3
 
1.9%
1.3 3
 
1.9%
0.75 2
 
1.3%
Other values (74) 94
61.0%
ValueCountFrequency (%)
0.049 12
7.8%
0.05 18
11.7%
0.06 2
 
1.3%
0.08 2
 
1.3%
0.1 5
 
3.2%
0.11 2
 
1.3%
0.12 1
 
0.6%
0.14 1
 
0.6%
0.15 1
 
0.6%
0.17 1
 
0.6%
ValueCountFrequency (%)
23 2
1.3%
22 2
1.3%
19 1
0.6%
12 1
0.6%
9.3 1
0.6%
8.5 1
0.6%
7.2 1
0.6%
7.1 1
0.6%
5.7 1
0.6%
5.6 1
0.6%

p_total_l_mg_l
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90872727
Minimum0.1
Maximum30.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:35.917468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1365
Q10.2625
median0.385
Q30.57
95-th percentile1.335
Maximum30.12
Range30.02
Interquartile range (IQR)0.3075

Descriptive statistics

Standard deviation3.4159541
Coefficient of variation (CV)3.7590531
Kurtosis69.689028
Mean0.90872727
Median Absolute Deviation (MAD)0.145
Skewness8.3081646
Sum139.944
Variance11.668742
MonotonicityNot monotonic
2024-11-09T12:33:36.262110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33 8
 
5.2%
0.23 6
 
3.9%
0.24 5
 
3.2%
0.28 4
 
2.6%
0.27 4
 
2.6%
0.1 4
 
2.6%
0.4 4
 
2.6%
1.2 4
 
2.6%
0.36 4
 
2.6%
0.57 3
 
1.9%
Other values (67) 108
70.1%
ValueCountFrequency (%)
0.1 4
2.6%
0.11 1
 
0.6%
0.12 1
 
0.6%
0.13 2
1.3%
0.14 1
 
0.6%
0.15 1
 
0.6%
0.17 2
1.3%
0.18 2
1.3%
0.19 3
1.9%
0.2 1
 
0.6%
ValueCountFrequency (%)
30.12 2
1.3%
6.504 1
 
0.6%
2.8 1
 
0.6%
1.9 1
 
0.6%
1.5 1
 
0.6%
1.4 2
1.3%
1.3 2
1.3%
1.2 4
2.6%
1.1 1
 
0.6%
1.06 1
 
0.6%

fosf_ortofos_mg_l
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41545455
Minimum0.1
Maximum2.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:36.621912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1365
Q10.23
median0.33
Q30.4975
95-th percentile0.924
Maximum2.6
Range2.5
Interquartile range (IQR)0.2675

Descriptive statistics

Standard deviation0.30868494
Coefficient of variation (CV)0.74300533
Kurtosis16.932136
Mean0.41545455
Median Absolute Deviation (MAD)0.115
Skewness3.2483309
Sum63.98
Variance0.095286393
MonotonicityNot monotonic
2024-11-09T12:33:37.525401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32 7
 
4.5%
0.2 7
 
4.5%
0.27 7
 
4.5%
0.54 6
 
3.9%
0.31 6
 
3.9%
0.18 5
 
3.2%
0.39 5
 
3.2%
0.4 5
 
3.2%
0.35 4
 
2.6%
0.24 4
 
2.6%
Other values (52) 98
63.6%
ValueCountFrequency (%)
0.1 4
2.6%
0.11 2
 
1.3%
0.12 1
 
0.6%
0.13 1
 
0.6%
0.14 1
 
0.6%
0.15 3
1.9%
0.16 2
 
1.3%
0.17 2
 
1.3%
0.18 5
3.2%
0.19 3
1.9%
ValueCountFrequency (%)
2.6 1
0.6%
1.4 2
1.3%
1.3 1
0.6%
1.2 2
1.3%
1 1
0.6%
0.95 1
0.6%
0.91 1
0.6%
0.88 1
0.6%
0.86 2
1.3%
0.85 2
1.3%

dbo_mg_l
Real number (ℝ)

Distinct71
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7963636
Minimum1.9
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:37.850185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile1.9
Q12.78
median5
Q37.075
95-th percentile14
Maximum42
Range40.1
Interquartile range (IQR)4.295

Descriptive statistics

Standard deviation4.6810666
Coefficient of variation (CV)0.80758676
Kurtosis23.36681
Mean5.7963636
Median Absolute Deviation (MAD)2.22
Skewness3.7376169
Sum892.64
Variance21.912384
MonotonicityNot monotonic
2024-11-09T12:33:38.181623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 16
 
10.4%
5 11
 
7.1%
1.9 10
 
6.5%
2.78 10
 
6.5%
12 5
 
3.2%
4 4
 
2.6%
5.24 4
 
2.6%
6.5 4
 
2.6%
2.8 3
 
1.9%
14 3
 
1.9%
Other values (61) 84
54.5%
ValueCountFrequency (%)
1.9 10
6.5%
2 16
10.4%
2.16 1
 
0.6%
2.3 1
 
0.6%
2.4 2
 
1.3%
2.5 2
 
1.3%
2.6 1
 
0.6%
2.78 10
6.5%
2.8 3
 
1.9%
2.96 1
 
0.6%
ValueCountFrequency (%)
42 1
 
0.6%
21 1
 
0.6%
18 2
 
1.3%
16 1
 
0.6%
15 1
 
0.6%
14 3
1.9%
12 5
3.2%
11 3
1.9%
10 2
 
1.3%
9.4 3
1.9%

dqo_mg_l
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.188312
Minimum29
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:38.491450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile29
Q130
median30
Q352.25
95-th percentile85.4
Maximum180
Range151
Interquartile range (IQR)22.25

Descriptive statistics

Standard deviation23.477084
Coefficient of variation (CV)0.53129624
Kurtosis8.5813656
Mean44.188312
Median Absolute Deviation (MAD)1
Skewness2.4777461
Sum6805
Variance551.17346
MonotonicityNot monotonic
2024-11-09T12:33:38.783013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
30 54
35.1%
29 27
17.5%
50 7
 
4.5%
39 4
 
2.6%
33 3
 
1.9%
36 3
 
1.9%
48 3
 
1.9%
59 2
 
1.3%
63 2
 
1.3%
80 2
 
1.3%
Other values (38) 47
30.5%
ValueCountFrequency (%)
29 27
17.5%
30 54
35.1%
31 1
 
0.6%
32 2
 
1.3%
33 3
 
1.9%
34 2
 
1.3%
35 1
 
0.6%
36 3
 
1.9%
37 1
 
0.6%
39 4
 
2.6%
ValueCountFrequency (%)
180 1
0.6%
135 1
0.6%
130 1
0.6%
110 1
0.6%
94 1
0.6%
90 1
0.6%
89 1
0.6%
88 1
0.6%
84 1
0.6%
82 2
1.3%

turbiedad_ntu
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.342857
Minimum2.5
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:39.114979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile8.15
Q117
median27.5
Q345
95-th percentile85
Maximum130
Range127.5
Interquartile range (IQR)28

Descriptive statistics

Standard deviation24.213727
Coefficient of variation (CV)0.7050586
Kurtosis1.4434664
Mean34.342857
Median Absolute Deviation (MAD)11.5
Skewness1.3036196
Sum5288.8
Variance586.30456
MonotonicityNot monotonic
2024-11-09T12:33:39.464800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 7
 
4.5%
19 6
 
3.9%
45 6
 
3.9%
22 6
 
3.9%
28 5
 
3.2%
23 5
 
3.2%
90 5
 
3.2%
60 5
 
3.2%
13 5
 
3.2%
26 5
 
3.2%
Other values (50) 99
64.3%
ValueCountFrequency (%)
2.5 1
0.6%
3.3 1
0.6%
4.1 1
0.6%
6 1
0.6%
6.1 1
0.6%
7.3 1
0.6%
7.5 2
1.3%
8.5 1
0.6%
8.6 1
0.6%
8.9 1
0.6%
ValueCountFrequency (%)
130 1
 
0.6%
110 1
 
0.6%
90 5
3.2%
85 2
 
1.3%
84 1
 
0.6%
80 2
 
1.3%
75 3
1.9%
71 1
 
0.6%
70 3
1.9%
67 1
 
0.6%

cr_total_mg_l
Real number (ℝ)

Distinct15
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5441039
Minimum0.005
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:39.734420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.005
Q10.005
median0.005
Q30.005
95-th percentile6
Maximum12
Range11.995
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9460255
Coefficient of variation (CV)3.5765697
Kurtosis14.250188
Mean0.5441039
Median Absolute Deviation (MAD)0
Skewness3.7688688
Sum83.792
Variance3.7870153
MonotonicityNot monotonic
2024-11-09T12:33:40.016807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.005 117
76.0%
0.006 8
 
5.2%
0.007 7
 
4.5%
6 4
 
2.6%
0.008 3
 
1.9%
5 3
 
1.9%
0.01 2
 
1.3%
0.011 2
 
1.3%
7 2
 
1.3%
0.009 1
 
0.6%
Other values (5) 5
 
3.2%
ValueCountFrequency (%)
0.005 117
76.0%
0.006 8
 
5.2%
0.007 7
 
4.5%
0.008 3
 
1.9%
0.009 1
 
0.6%
0.01 2
 
1.3%
0.011 2
 
1.3%
0.015 1
 
0.6%
0.02 1
 
0.6%
5 3
 
1.9%
ValueCountFrequency (%)
12 1
 
0.6%
10 1
 
0.6%
8 1
 
0.6%
7 2
1.3%
6 4
2.6%
5 3
1.9%
0.02 1
 
0.6%
0.015 1
 
0.6%
0.011 2
1.3%
0.01 2
1.3%

clorofila_a_ug_l
Real number (ℝ)

High correlation  Zeros 

Distinct72
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean273.63857
Minimum0
Maximum6410
Zeros5
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:40.348435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.265
Q110
median10
Q352.575
95-th percentile1515.5
Maximum6410
Range6410
Interquartile range (IQR)42.575

Descriptive statistics

Standard deviation820.55074
Coefficient of variation (CV)2.9986662
Kurtosis27.056022
Mean273.63857
Median Absolute Deviation (MAD)1.75
Skewness4.7769431
Sum42140.34
Variance673303.52
MonotonicityNot monotonic
2024-11-09T12:33:40.689420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 74
48.1%
0 5
 
3.2%
0.3 3
 
1.9%
350 2
 
1.3%
0.6 2
 
1.3%
0.2 2
 
1.3%
29.82 1
 
0.6%
31.56 1
 
0.6%
130.2 1
 
0.6%
20.7 1
 
0.6%
Other values (62) 62
40.3%
ValueCountFrequency (%)
0 5
3.2%
0.1 1
 
0.6%
0.2 2
 
1.3%
0.3 3
1.9%
0.4 1
 
0.6%
0.5 1
 
0.6%
0.6 2
 
1.3%
0.7 1
 
0.6%
0.8 1
 
0.6%
1 1
 
0.6%
ValueCountFrequency (%)
6410 1
0.6%
4650 1
0.6%
3590 1
0.6%
2900 1
0.6%
2500 1
0.6%
2130 1
0.6%
1960 1
0.6%
1730 1
0.6%
1400 1
0.6%
1319.7 1
0.6%

microcistina_ug_l
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18577922
Minimum0.15
Maximum1.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:40.934514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.15
Q10.15
median0.15
Q30.1875
95-th percentile0.2
Maximum1.67
Range1.52
Interquartile range (IQR)0.0375

Descriptive statistics

Standard deviation0.15665116
Coefficient of variation (CV)0.8432114
Kurtosis61.671029
Mean0.18577922
Median Absolute Deviation (MAD)0
Skewness7.5070192
Sum28.61
Variance0.024539585
MonotonicityNot monotonic
2024-11-09T12:33:41.208187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.15 109
70.8%
0.2 31
 
20.1%
0.16 4
 
2.6%
0.19 2
 
1.3%
1 2
 
1.3%
0.4 1
 
0.6%
0.17 1
 
0.6%
0.3 1
 
0.6%
1.67 1
 
0.6%
0.18 1
 
0.6%
ValueCountFrequency (%)
0.15 109
70.8%
0.16 4
 
2.6%
0.17 1
 
0.6%
0.18 1
 
0.6%
0.19 2
 
1.3%
0.2 31
 
20.1%
0.3 1
 
0.6%
0.32 1
 
0.6%
0.4 1
 
0.6%
1 2
 
1.3%
ValueCountFrequency (%)
1.67 1
 
0.6%
1 2
 
1.3%
0.4 1
 
0.6%
0.32 1
 
0.6%
0.3 1
 
0.6%
0.2 31
20.1%
0.19 2
 
1.3%
0.18 1
 
0.6%
0.17 1
 
0.6%
0.16 4
 
2.6%

ica
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.071429
Minimum23
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-11-09T12:33:41.515132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile33
Q138
median42
Q350
95-th percentile59.35
Maximum76
Range53
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.9487335
Coefficient of variation (CV)0.20305068
Kurtosis0.32568788
Mean44.071429
Median Absolute Deviation (MAD)5
Skewness0.65752044
Sum6787
Variance80.079832
MonotonicityNot monotonic
2024-11-09T12:33:41.816450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
40 13
 
8.4%
42 10
 
6.5%
37 10
 
6.5%
36 9
 
5.8%
38 8
 
5.2%
46 7
 
4.5%
41 7
 
4.5%
39 7
 
4.5%
45 7
 
4.5%
55 6
 
3.9%
Other values (28) 70
45.5%
ValueCountFrequency (%)
23 1
 
0.6%
25 1
 
0.6%
29 2
 
1.3%
31 1
 
0.6%
32 2
 
1.3%
33 4
 
2.6%
34 3
 
1.9%
35 5
3.2%
36 9
5.8%
37 10
6.5%
ValueCountFrequency (%)
76 1
 
0.6%
67 1
 
0.6%
64 1
 
0.6%
62 1
 
0.6%
61 2
 
1.3%
60 2
 
1.3%
59 5
3.2%
58 4
2.6%
57 1
 
0.6%
56 2
 
1.3%
Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
117 
True
37 
ValueCountFrequency (%)
False 117
76.0%
True 37
 
24.0%
2024-11-09T12:33:42.051033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

campaña_Verano
Boolean

High correlation 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
115 
True
39 
ValueCountFrequency (%)
False 115
74.7%
True 39
 
25.3%
2024-11-09T12:33:42.273830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

campaña_invierno
Boolean

High correlation 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
117 
True
37 
ValueCountFrequency (%)
False 117
76.0%
True 37
 
24.0%
2024-11-09T12:33:42.489587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

campaña_otoño
Boolean

High correlation 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
113 
True
41 
ValueCountFrequency (%)
False 113
73.4%
True 41
 
26.6%
2024-11-09T12:33:42.712570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2024-11-09T12:33:23.234544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:11.383040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:16.810456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:21.123307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:25.431852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:29.606453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:33.824985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:38.216532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:42.200630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:46.601763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:50.465082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:54.328188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:58.771041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:02.903895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:06.785613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:10.684965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:14.602216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:19.165006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:23.400610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:11.598530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:17.016155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:21.356155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:25.614892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:29.833334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:34.025007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:38.475637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:42.424181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-09T12:32:52.990459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:57.048201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:01.500813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:05.517009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:09.384700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:13.314208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:17.802213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:21.861000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:25.926213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:15.584164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:20.026708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:24.315135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:28.343360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:32.699619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:36.885504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:41.114191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:45.511762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:49.266865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:53.184591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:57.276037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:01.726492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:05.748390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:09.584506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:13.552337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:18.034643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:22.099326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:26.149879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:15.848498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:20.246017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:24.541714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:28.619226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:32.928846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:37.164983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:41.333561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:45.731621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:49.531777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:53.419527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:57.466651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:01.951188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:05.931584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:09.799569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:13.759742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:18.267894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:22.333448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:26.400844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:16.100491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:20.452731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:24.765008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:28.867874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:33.131771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:37.418864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:41.552673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:45.950860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:49.751369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:53.620079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:57.702465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:02.183333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:06.145400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:10.034386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:13.979099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:18.485936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:22.565084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:26.624326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:16.315889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:20.660594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:24.989191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:29.089853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:33.365391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:37.717019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:41.765008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:46.155050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:49.983430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:53.851228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:57.950277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:02.431893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:06.348320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:10.262941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:14.184684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:18.715243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:22.798142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:26.879103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:16.574130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:20.917020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:25.233361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:29.331917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:33.617177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:37.984328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:41.998951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:46.395366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:50.216556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:54.104905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:32:58.201511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:02.666718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:06.574654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:10.481722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:14.418288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:18.948431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-09T12:33:23.024706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-09T12:33:42.964793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
campaña_Primaveracampaña_Veranocampaña_inviernocampaña_otoñoclorofila_a_ug_lcolif_fecales_ufc_100mlcolorcr_total_mg_ldbo_mg_ldqo_mg_lenteroc_ufc_100mlescher_coli_ufc_100mlespumasfosf_ortofos_mg_licamat_suspmicrocistina_ug_lnh4_mg_lnitrato_mg_lodoloresp_total_l_mg_lphtem_aguatem_aireturbiedad_ntu
campaña_Primavera1.0000.3000.2880.3120.0000.0000.0370.4270.1110.2270.0000.0000.0000.1080.2060.0000.0000.0000.2940.1680.1090.1750.0000.4710.4430.318
campaña_Verano0.3001.0000.3000.3250.0000.0000.2120.0000.0000.2250.1020.0000.0000.0000.3310.0000.1610.0000.1780.0000.0000.0000.1500.8650.7640.144
campaña_invierno0.2880.3001.0000.3120.5170.2840.0000.0000.2880.1920.0000.0000.0000.2960.4140.0000.0000.4930.5160.0330.0430.0000.1200.6970.3240.242
campaña_otoño0.3120.3250.3121.0000.0000.0000.0000.0000.0000.7750.0000.2490.0000.0000.0210.0000.0880.0000.3390.1470.0000.0980.1470.4970.2750.530
clorofila_a_ug_l0.0000.0000.5170.0001.0000.1060.000-0.0300.211-0.119-0.290-0.3660.0000.164-0.2360.0000.1960.0140.3690.1910.0000.1420.295-0.239-0.045-0.085
colif_fecales_ufc_100ml0.0000.0000.2840.0000.1061.0000.0700.2150.3540.2150.2710.2250.2570.329-0.6030.131-0.2580.3840.229-0.3190.2930.366-0.169-0.3180.080-0.152
color0.0370.2120.0000.0000.0000.0701.0000.0000.0000.0000.4310.3360.3990.1860.3630.2470.1070.3540.0000.2730.4730.1300.2590.3000.1810.000
cr_total_mg_l0.4270.0000.0000.000-0.0300.2150.0001.0000.1930.201-0.0060.1130.0000.301-0.3520.000-0.2930.124-0.038-0.0020.0000.398-0.013-0.204-0.2270.166
dbo_mg_l0.1110.0000.2880.0000.2110.3540.0000.1931.000-0.021-0.0120.0850.1940.449-0.3730.000-0.0530.3210.0720.0290.3720.4490.204-0.114-0.056-0.305
dqo_mg_l0.2270.2250.1920.775-0.1190.2150.0000.201-0.0211.0000.1720.2560.0000.010-0.1830.000-0.372-0.0610.1830.0320.0000.1210.007-0.302-0.2460.331
enteroc_ufc_100ml0.0000.1020.0000.000-0.2900.2710.431-0.006-0.0120.1721.0000.4780.602-0.043-0.1410.2890.0790.000-0.033-0.2660.3550.054-0.0940.1030.1600.032
escher_coli_ufc_100ml0.0000.0000.0000.249-0.3660.2250.3360.1130.0850.2560.4781.0000.405-0.033-0.0660.205-0.1230.026-0.115-0.2400.2180.151-0.0920.130-0.0110.068
espumas0.0000.0000.0000.0000.0000.2570.3990.0000.1940.0000.6020.4051.0000.3680.3380.2400.0000.4610.0000.0000.4190.0000.1670.2130.0000.000
fosf_ortofos_mg_l0.1080.0000.2960.0000.1640.3290.1860.3010.4490.010-0.043-0.0330.3681.000-0.5030.0000.1580.2980.039-0.2140.3450.7760.137-0.0570.055-0.404
ica0.2060.3310.4140.021-0.236-0.6030.363-0.352-0.373-0.183-0.141-0.0660.338-0.5031.0000.2700.210-0.494-0.1560.2560.560-0.575-0.0070.3340.0490.277
mat_susp0.0000.0000.0000.0000.0000.1310.2470.0000.0000.0000.2890.2050.2400.0000.2701.0000.0000.1520.0000.2720.2690.0000.2140.3070.2540.000
microcistina_ug_l0.0000.1610.0000.0880.196-0.2580.107-0.293-0.053-0.3720.079-0.1230.0000.1580.2100.0001.000-0.335-0.101-0.1080.0000.0400.0620.6040.555-0.079
nh4_mg_l0.0000.0000.4930.0000.0140.3840.3540.1240.321-0.0610.0000.0260.4610.298-0.4940.152-0.3351.000-0.070-0.2600.3840.321-0.152-0.257-0.150-0.452
nitrato_mg_l0.2940.1780.5160.3390.3690.2290.000-0.0380.0720.183-0.033-0.1150.0000.039-0.1560.000-0.101-0.0701.0000.1360.0000.0220.149-0.2420.039-0.029
od0.1680.0000.0330.1470.191-0.3190.273-0.0020.0290.032-0.266-0.2400.000-0.2140.2560.272-0.108-0.2600.1361.0000.100-0.2330.587-0.224-0.1370.260
olores0.1090.0000.0430.0000.0000.2930.4730.0000.3720.0000.3550.2180.4190.3450.5600.2690.0000.3840.0000.1001.0000.0000.2620.0000.0000.150
p_total_l_mg_l0.1750.0000.0000.0980.1420.3660.1300.3980.4490.1210.0540.1510.0000.776-0.5750.0000.0400.3210.022-0.2330.0001.0000.107-0.073-0.011-0.303
ph0.0000.1500.1200.1470.295-0.1690.259-0.0130.2040.007-0.094-0.0920.1670.137-0.0070.2140.062-0.1520.1490.5870.2620.1071.000-0.207-0.1990.017
tem_agua0.4710.8650.6970.497-0.239-0.3180.300-0.204-0.114-0.3020.1030.1300.213-0.0570.3340.3070.604-0.257-0.242-0.2240.000-0.073-0.2071.0000.653-0.017
tem_aire0.4430.7640.3240.275-0.0450.0800.181-0.227-0.056-0.2460.160-0.0110.0000.0550.0490.2540.555-0.1500.039-0.1370.000-0.011-0.1990.6531.000-0.061
turbiedad_ntu0.3180.1440.2420.530-0.085-0.1520.0000.166-0.3050.3310.0320.0680.000-0.4040.2770.000-0.079-0.452-0.0290.2600.150-0.3030.017-0.017-0.0611.000

Missing values

2024-11-09T12:33:27.283569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-09T12:33:28.272335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

tem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntucr_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacampaña_Primaveracampaña_Veranocampaña_inviernocampaña_otoño
024.523.35.306.56FalseFalseFalseTrue2200.0100.0130.02.90.4200.230.156.229.090.00.00510.00.255.0FalseTrueFalseFalse
125.423.32.256.56TrueTrueFalseFalse1200.0200.0400.03.30.5100.410.355.829.034.00.00510.00.242.0FalseTrueFalseFalse
224.623.32.946.59FalseTrueFalseFalse1800.0200.0580.06.50.0500.590.541.929.017.00.00510.00.245.0FalseTrueFalseFalse
325.223.32.227.45TrueTrueFalseFalse1400.0100.0300.07.41.0000.380.405.829.023.00.00510.00.246.0FalseTrueFalseFalse
424.120.01.026.39FalseTrueFalseTrue1100.0100.0370.08.80.0490.550.542.659.018.00.00510.00.244.0FalseTrueFalseFalse
524.923.33.506.53FalseFalseFalseTrue3200.0200.0750.04.43.5001.100.913.9130.08.90.00510.00.240.0FalseTrueFalseFalse
624.520.01.506.54FalseTrueFalseTrue18000.01500.0100.05.62.0000.730.603.542.012.00.00510.00.435.0FalseTrueFalseFalse
724.521.06.306.48FalseTrueFalseFalse1000.0200.01200.03.10.0490.170.165.569.090.00.00510.00.246.0FalseTrueFalseFalse
823.421.04.496.76FalseFalseFalseFalse400.0100.0220.01.90.1000.210.191.929.039.00.00510.00.258.0FalseTrueFalseFalse
921.523.03.856.66FalseFalseFalseTrue2200.0100.0270.05.40.0490.280.391.929.028.00.00510.00.251.0FalseTrueFalseFalse
tem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntucr_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacampaña_Primaveracampaña_Veranocampaña_inviernocampaña_otoño
15817.4810.08.6567.890FalseFalseFalseFalse150.0100.030.06.20.140.600.382.7872.039.010.00074.20.1541.0TrueFalseFalseFalse
15916.007.011.0509.170FalseFalseFalseFalse210.080.090.05.90.150.360.212.7833.031.00.00536.50.1938.0TrueFalseFalseFalse
16017.206.08.3808.090FalseFalseFalseFalse95.035.050.05.70.410.290.232.7846.026.00.00529.40.1541.0TrueFalseFalseFalse
16118.004.07.3607.870FalseFalseFalseFalse800.0700.0220.05.42.300.360.234.0030.023.00.00516.70.1537.0TrueFalseFalseFalse
16217.105.08.9808.050FalseFalseFalseFalse130.030.045.06.10.400.240.242.7830.039.00.0050.60.1554.0TrueFalseFalseFalse
16310.0012.06.7947.856FalseFalseFalseTrue800.0600.0400.06.90.380.240.244.0030.023.00.0052.10.1543.0TrueFalseFalseFalse
16410.0012.04.2387.414FalseTrueFalseTrue80000.080000.012000.05.21.2030.120.393.9031.018.20.00520.20.1537.0TrueFalseFalseFalse
16510.0012.06.6047.478FalseFalseFalseTrue1400.01000.0380.04.60.800.450.437.2630.040.00.0050.20.1549.0TrueFalseFalseFalse
16610.0012.07.7767.460FalseFalseFalseTrue1800.01500.0500.05.20.550.270.276.7839.090.05.00010.50.1539.0TrueFalseFalseFalse
16710.0012.07.4387.850FalseFalseFalseTrue900.0600.0480.05.10.210.480.354.7630.070.05.00048.00.1534.0TrueFalseFalseFalse